app.py

from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
import torch

# Load pre-trained model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("ahmed792002/alzheimers_memory_support_ai")
model = AutoModelForCausalLM.from_pretrained("ahmed792002/alzheimers_memory_support_ai")

# Chatbot function
def chatbot(query, history, system_message, max_length, temperature, top_k, top_p):
    """
    Processes a user query through the specified model to generate a response.
    """
    
    # Tokenize input query
    input_ids = tokenizer.encode(query, return_tensors="pt")
    response = '.'
    while response=='.':
        # Generate text using the model
        final_outputs = model.generate(
            input_ids,
            do_sample=True,
            max_length=int(max_length),  # Convert max_length to integer
            temperature=float(temperature),  # Convert temperature to float
            top_k=int(top_k),  # Convert top_k to integer
            top_p=float(top_p),  # Convert top_p to float
            pad_token_id=tokenizer.pad_token_id,
        )
        # Decode generated text
        response = tokenizer.decode(final_outputs[0], skip_special_tokens=True)
        response = response.split('"')[1]
            
    return response

# Gradio ChatInterface
demo = gr.ChatInterface(
    chatbot,
    additional_inputs=[
        gr.Textbox(value="You are a friendly chatbot.", label="System message"),
        gr.Slider(128, 1024, value=256, step=64, label="Max Length"),  # Slider for max_length
        gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature"),  # Slider for temperature
        gr.Slider(1, 100, value=50, step=1, label="Top-K"),  # Slider for top_k
        gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-P"),  # Slider for top_p
    ],
)

if __name__ == "__main__":
    demo.launch(share=True)  # Set `share=True` to create a public link